A Heuristic for Dynamic Output Predictive Control Design for Uncertain Nonlinear Systems

02/03/2021
by   Mazen Alamir, et al.
0

In this paper, a simple heuristic is proposed for the design of uncertainty aware predictive controllers for nonlinear models involving uncertain parameters. The method relies on Machine Learning-based approximation of ideal deterministic MPC solutions with perfectly known parameters. An efficient construction of the learning data set from these off-line solutions is proposed in which each solution provides many samples in the learning data. This enables a drastic reduction of the required number of Non Linear Programming problems to be solved off-line while explicitly exploiting the statistics of the parameters dispersion. The learning data is then used to design a fast on-line output dynamic feedback that explicitly incorporate information of the statistics of the parameters dispersion. An example is provided to illustrate the efficiency and the relevance of the proposed framework. It is in particular shown that the proposed solution recovers up to 78% of the expected advantage of having a perfect knowledge of the parameters compared to nominal design.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 6

10/01/2021

RLO-MPC: Robust Learning-Based Output Feedback MPC for Improving the Performance of Uncertain Systems in Iterative Tasks

In this work we address the problem of performing a repetitive task when...
03/22/2018

Linear model predictive safety certification for learning-based control

While it has been repeatedly shown that learning-based controllers can p...
11/22/2019

Robust Learning-based Predictive Control for Constrained Nonlinear Systems

The integration of machine learning methods and Model Predictive Control...
07/24/2020

Anticipating the Long-Term Effect of Online Learning in Control

Control schemes that learn using measurement data collected online are i...
11/20/2019

Fast Non-Parametric Learning to Accelerate Mixed-Integer Programming for Online Hybrid Model Predictive Control

Today's fast linear algebra and numerical optimization tools have pushed...
07/17/2019

Dynamic optimization with side information

We present a data-driven framework for incorporating side information in...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.